SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We provide more extensive visual results comparison in Figure 9 (DIS [35] test set), Figure 10 (zero-shot setting in COCO), Figure 11 (noised box input) and Figure 12 (zero-shot setting in HRSOD [51], NDD20 [41] and web images which cover objects with various structure complexities in diverse environments. In Figure 13 and Figure 14, we provide the zero-shot video segmentation results comparison on DAVIS 2017 and YTVIS 2019 benchmarks respectively. Besides, we include the dark underwater environment in NDD20 [41] and randomly selected web images in Figure 12, showing that the zero-shot segmentation power in SAM is well preserved by HQ-SAM. In Figure 12, we also include two failure cases in the rightmost two columns of the third row and bottom row, where HQ-SAM improves over SAM, but still cannot achieve fully correct mask prediction.
Figure 10: Visual results comparison between SAM (top row) vs. HQ-SAM (bottom row) on COCO val set in zero-shot setting, using a SOTA detector FocalNet-DINO [53] trained on the COCO dataset as our box prompt generator. HQ-SAM predicts masks with higher quality than SAM with less mask artifacts.
Figure 11: Visual results comparison between SAM (top row) vs. HQ-SAM (bottom row) with both the GT and noised green box prompt. HQ-SAM produces much more consistent and robust segmentation results regarding to the noises in the input boxes.
Figure 12: Visual results comparison between SAM (top row and third row) vs. HQ-SAM (second row and bottom row) in zero-shot setting, given the same yellow box or point prompt. HQ-SAM produces significantly more detailed preserving masks while fixing mask errors with broken holes. The rightmost two columns in the third row and bottom row show two failure cases of HQ-SAM in extremely dark environments or very tiny metal rods.
Figure 13: Visual results comparison between SAM vs. HQ-SAM on video object segmentation benchmark DAVIS 2017 in zero-shot setting, given the same video boxes prompts generated by the pre-trained XMem [7].
Figure 14: Visual results comparison between SAM vs. HQ-SAM on video instance segmentation benchmark YTVIS 2019 in zero-shot setting, given the same video boxes prompts generated by the pre-trained Mask2Former [4].